Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [18]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [19]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[19]:
<matplotlib.image.AxesImage at 0x7f09d7fcdb70>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [20]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[20]:
<matplotlib.image.AxesImage at 0x7f098bfd4240>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [21]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [22]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learn_rate = tf.placeholder(tf.float32, (None), name='learn_rate')


    return inputs_real, inputs_z, learn_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [23]:
def discriminator(images, reuse=False):
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        images1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * images1, images1)
        
        images2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(images2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        images3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(images3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)

        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [24]:
def generator(z, out_channel_dim, is_train=True):
    alpha = 0.2
    reuse = not is_train
    with tf.variable_scope('generator', reuse=reuse):

        x1 = tf.layers.dense(z, 4*4*512)
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 4, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        x3 = tf.layers.conv2d_transpose(x3, out_channel_dim, 4, strides=2, padding="same")        

        logits = x3        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [25]:
def model_loss(input_real, input_z, out_channel_dim):
    smooth = 0.1
    _, d_logits_real = discriminator(input_real, reuse=False)
    fake = generator(input_z, out_channel_dim, is_train=True)
    d_logits_fake = discriminator(fake, reuse=True)

    d_loss_real = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                              labels=tf.ones_like(d_logits_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
                      tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                              labels=tf.zeros_like(d_logits_fake)))
    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(
                 tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                                         labels=tf.ones_like(d_logits_fake)))
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [26]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    g_update_ops = [var for var in all_update_ops if var.name.startswith('generator')]
    d_update_ops = [var for var in all_update_ops if var.name.startswith('discriminator')]

    with tf.control_dependencies(d_update_ops):
        d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(g_update_ops):
        g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss, var_list=g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [27]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [86]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    inputs_real, inputs_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(inputs_real, inputs_z, data_shape[-1])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    print_every=25
    show_every=100

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for e in range(epochs):
            for x in get_batches(batch_size):
                steps += 1
                x = x * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                _ = sess.run(d_train_opt, feed_dict={inputs_real: x, inputs_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z, lr:learning_rate})

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({inputs_z:batch_z, inputs_real: x})
                    train_loss_g = g_loss.eval({inputs_z: batch_z})

                    print("Epoch {}/{}...".format(e+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, inputs_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [87]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.7414... Generator Loss: 1.6547
Epoch 1/2... Discriminator Loss: 0.7395... Generator Loss: 3.8511
Epoch 1/2... Discriminator Loss: 0.8734... Generator Loss: 2.8650
Epoch 1/2... Discriminator Loss: 0.8052... Generator Loss: 1.8631
Epoch 1/2... Discriminator Loss: 0.8873... Generator Loss: 1.4987
Epoch 1/2... Discriminator Loss: 0.8369... Generator Loss: 1.4947
Epoch 1/2... Discriminator Loss: 0.9106... Generator Loss: 1.3661
Epoch 1/2... Discriminator Loss: 0.8695... Generator Loss: 1.5488
Epoch 1/2... Discriminator Loss: 0.9816... Generator Loss: 1.0619
Epoch 1/2... Discriminator Loss: 1.2902... Generator Loss: 1.3464
Epoch 1/2... Discriminator Loss: 1.1437... Generator Loss: 0.7156
Epoch 1/2... Discriminator Loss: 1.1086... Generator Loss: 0.9853
Epoch 1/2... Discriminator Loss: 1.1842... Generator Loss: 1.3386
Epoch 1/2... Discriminator Loss: 1.0639... Generator Loss: 1.0589
Epoch 1/2... Discriminator Loss: 1.0645... Generator Loss: 0.8868
Epoch 1/2... Discriminator Loss: 1.0918... Generator Loss: 1.1129
Epoch 1/2... Discriminator Loss: 1.1406... Generator Loss: 0.9672
Epoch 1/2... Discriminator Loss: 1.1448... Generator Loss: 1.4186
Epoch 2/2... Discriminator Loss: 1.1091... Generator Loss: 0.8351
Epoch 2/2... Discriminator Loss: 1.2326... Generator Loss: 1.4461
Epoch 2/2... Discriminator Loss: 1.0993... Generator Loss: 0.8647
Epoch 2/2... Discriminator Loss: 1.2046... Generator Loss: 0.6181
Epoch 2/2... Discriminator Loss: 1.1907... Generator Loss: 0.6153
Epoch 2/2... Discriminator Loss: 1.2368... Generator Loss: 0.6044
Epoch 2/2... Discriminator Loss: 1.3687... Generator Loss: 0.4907
Epoch 2/2... Discriminator Loss: 1.1468... Generator Loss: 1.1963
Epoch 2/2... Discriminator Loss: 1.0997... Generator Loss: 0.7623
Epoch 2/2... Discriminator Loss: 1.0894... Generator Loss: 0.8706
Epoch 2/2... Discriminator Loss: 1.3318... Generator Loss: 0.5235
Epoch 2/2... Discriminator Loss: 1.1265... Generator Loss: 1.0740
Epoch 2/2... Discriminator Loss: 1.0517... Generator Loss: 0.9959
Epoch 2/2... Discriminator Loss: 1.1842... Generator Loss: 0.6184
Epoch 2/2... Discriminator Loss: 1.0531... Generator Loss: 0.8138
Epoch 2/2... Discriminator Loss: 0.9928... Generator Loss: 0.9831
Epoch 2/2... Discriminator Loss: 1.1603... Generator Loss: 0.7482
Epoch 2/2... Discriminator Loss: 1.1097... Generator Loss: 0.7325
Epoch 2/2... Discriminator Loss: 1.0213... Generator Loss: 1.2887

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [88]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/10... Discriminator Loss: 0.8263... Generator Loss: 2.8513
Epoch 1/10... Discriminator Loss: 1.1512... Generator Loss: 0.6899
Epoch 1/10... Discriminator Loss: 0.8096... Generator Loss: 2.2640
Epoch 1/10... Discriminator Loss: 0.8352... Generator Loss: 1.6892
Epoch 1/10... Discriminator Loss: 0.8422... Generator Loss: 1.7424
Epoch 1/10... Discriminator Loss: 0.7624... Generator Loss: 1.6399
Epoch 1/10... Discriminator Loss: 0.7806... Generator Loss: 1.8402
Epoch 1/10... Discriminator Loss: 1.1037... Generator Loss: 0.8061
Epoch 1/10... Discriminator Loss: 1.1122... Generator Loss: 0.7021
Epoch 1/10... Discriminator Loss: 0.9722... Generator Loss: 1.0926
Epoch 1/10... Discriminator Loss: 0.8968... Generator Loss: 1.4296
Epoch 1/10... Discriminator Loss: 1.0146... Generator Loss: 1.1441
Epoch 1/10... Discriminator Loss: 1.0330... Generator Loss: 1.0580
Epoch 1/10... Discriminator Loss: 1.2282... Generator Loss: 0.6800
Epoch 1/10... Discriminator Loss: 1.0284... Generator Loss: 1.1443
Epoch 1/10... Discriminator Loss: 1.3282... Generator Loss: 0.7194
Epoch 1/10... Discriminator Loss: 1.1888... Generator Loss: 0.7714
Epoch 1/10... Discriminator Loss: 1.0893... Generator Loss: 1.1228
Epoch 1/10... Discriminator Loss: 1.2175... Generator Loss: 0.7124
Epoch 1/10... Discriminator Loss: 1.3829... Generator Loss: 1.1474
Epoch 1/10... Discriminator Loss: 1.2777... Generator Loss: 0.7375
Epoch 1/10... Discriminator Loss: 1.3032... Generator Loss: 0.8194
Epoch 1/10... Discriminator Loss: 1.2918... Generator Loss: 0.8604
Epoch 1/10... Discriminator Loss: 1.2377... Generator Loss: 1.0365
Epoch 1/10... Discriminator Loss: 1.2285... Generator Loss: 0.9064
Epoch 1/10... Discriminator Loss: 1.5072... Generator Loss: 0.4229
Epoch 1/10... Discriminator Loss: 1.3456... Generator Loss: 0.6252
Epoch 1/10... Discriminator Loss: 1.2955... Generator Loss: 0.6301
Epoch 1/10... Discriminator Loss: 1.3246... Generator Loss: 0.7705
Epoch 1/10... Discriminator Loss: 1.2534... Generator Loss: 0.7184
Epoch 1/10... Discriminator Loss: 1.3434... Generator Loss: 0.5978
Epoch 1/10... Discriminator Loss: 1.2142... Generator Loss: 0.8042
Epoch 1/10... Discriminator Loss: 1.3414... Generator Loss: 0.8275
Epoch 1/10... Discriminator Loss: 1.3244... Generator Loss: 0.6761
Epoch 1/10... Discriminator Loss: 1.3925... Generator Loss: 0.5626
Epoch 1/10... Discriminator Loss: 1.3227... Generator Loss: 0.6500
Epoch 1/10... Discriminator Loss: 1.3020... Generator Loss: 0.7196
Epoch 1/10... Discriminator Loss: 1.3086... Generator Loss: 0.7798
Epoch 1/10... Discriminator Loss: 1.4446... Generator Loss: 0.6420
Epoch 1/10... Discriminator Loss: 1.2888... Generator Loss: 0.7060
Epoch 1/10... Discriminator Loss: 1.3078... Generator Loss: 0.6496
Epoch 1/10... Discriminator Loss: 1.2649... Generator Loss: 0.8091
Epoch 1/10... Discriminator Loss: 1.5081... Generator Loss: 0.4470
Epoch 1/10... Discriminator Loss: 1.3768... Generator Loss: 0.7376
Epoch 1/10... Discriminator Loss: 1.2247... Generator Loss: 1.1027
Epoch 1/10... Discriminator Loss: 1.3491... Generator Loss: 1.1216
Epoch 1/10... Discriminator Loss: 1.4347... Generator Loss: 0.5885
Epoch 1/10... Discriminator Loss: 1.1185... Generator Loss: 0.8857
Epoch 1/10... Discriminator Loss: 1.2663... Generator Loss: 0.7402
Epoch 1/10... Discriminator Loss: 1.4568... Generator Loss: 0.4881
Epoch 1/10... Discriminator Loss: 1.3654... Generator Loss: 0.5085
Epoch 1/10... Discriminator Loss: 1.3645... Generator Loss: 0.6295
Epoch 1/10... Discriminator Loss: 1.2512... Generator Loss: 0.8678
Epoch 1/10... Discriminator Loss: 1.0858... Generator Loss: 0.7973
Epoch 1/10... Discriminator Loss: 1.2664... Generator Loss: 0.6313
Epoch 1/10... Discriminator Loss: 1.2372... Generator Loss: 0.6283
Epoch 1/10... Discriminator Loss: 1.1970... Generator Loss: 0.8907
Epoch 1/10... Discriminator Loss: 1.5247... Generator Loss: 0.4006
Epoch 1/10... Discriminator Loss: 1.3318... Generator Loss: 0.8599
Epoch 1/10... Discriminator Loss: 1.1003... Generator Loss: 1.0188
Epoch 1/10... Discriminator Loss: 1.2655... Generator Loss: 0.9357
Epoch 1/10... Discriminator Loss: 1.3448... Generator Loss: 0.5073
Epoch 1/10... Discriminator Loss: 1.3177... Generator Loss: 0.7045
Epoch 2/10... Discriminator Loss: 1.2507... Generator Loss: 0.9084
Epoch 2/10... Discriminator Loss: 1.2860... Generator Loss: 0.7415
Epoch 2/10... Discriminator Loss: 1.2650... Generator Loss: 0.6109
Epoch 2/10... Discriminator Loss: 1.2753... Generator Loss: 0.8443
Epoch 2/10... Discriminator Loss: 1.1842... Generator Loss: 0.8441
Epoch 2/10... Discriminator Loss: 1.1664... Generator Loss: 0.8956
Epoch 2/10... Discriminator Loss: 1.4710... Generator Loss: 0.8883
Epoch 2/10... Discriminator Loss: 1.2793... Generator Loss: 0.6840
Epoch 2/10... Discriminator Loss: 1.1885... Generator Loss: 1.0294
Epoch 2/10... Discriminator Loss: 1.3541... Generator Loss: 0.5459
Epoch 2/10... Discriminator Loss: 1.1985... Generator Loss: 0.7767
Epoch 2/10... Discriminator Loss: 1.3594... Generator Loss: 0.9183
Epoch 2/10... Discriminator Loss: 1.2174... Generator Loss: 0.6623
Epoch 2/10... Discriminator Loss: 1.2873... Generator Loss: 0.6389
Epoch 2/10... Discriminator Loss: 1.3097... Generator Loss: 0.7067
Epoch 2/10... Discriminator Loss: 1.3240... Generator Loss: 0.7630
Epoch 2/10... Discriminator Loss: 1.1606... Generator Loss: 0.7478
Epoch 2/10... Discriminator Loss: 1.2458... Generator Loss: 0.7779
Epoch 2/10... Discriminator Loss: 1.3009... Generator Loss: 0.7864
Epoch 2/10... Discriminator Loss: 1.3638... Generator Loss: 0.5498
Epoch 2/10... Discriminator Loss: 1.2023... Generator Loss: 0.9279
Epoch 2/10... Discriminator Loss: 1.3439... Generator Loss: 0.7512
Epoch 2/10... Discriminator Loss: 1.1652... Generator Loss: 0.9361
Epoch 2/10... Discriminator Loss: 1.2721... Generator Loss: 0.5911
Epoch 2/10... Discriminator Loss: 1.1744... Generator Loss: 0.8327
Epoch 2/10... Discriminator Loss: 1.3962... Generator Loss: 0.4840
Epoch 2/10... Discriminator Loss: 1.3341... Generator Loss: 0.5556
Epoch 2/10... Discriminator Loss: 1.4068... Generator Loss: 0.5113
Epoch 2/10... Discriminator Loss: 1.2707... Generator Loss: 0.7620
Epoch 2/10... Discriminator Loss: 1.3188... Generator Loss: 0.6182
Epoch 2/10... Discriminator Loss: 1.3374... Generator Loss: 0.5518
Epoch 2/10... Discriminator Loss: 1.2026... Generator Loss: 0.7281
Epoch 2/10... Discriminator Loss: 1.2458... Generator Loss: 0.9121
Epoch 2/10... Discriminator Loss: 1.2122... Generator Loss: 0.8956
Epoch 2/10... Discriminator Loss: 1.3199... Generator Loss: 0.7335
Epoch 2/10... Discriminator Loss: 1.3183... Generator Loss: 0.8042
Epoch 2/10... Discriminator Loss: 1.2095... Generator Loss: 0.8177
Epoch 2/10... Discriminator Loss: 1.2997... Generator Loss: 0.6751
Epoch 2/10... Discriminator Loss: 1.1298... Generator Loss: 0.9298
Epoch 2/10... Discriminator Loss: 1.1108... Generator Loss: 0.8231
Epoch 2/10... Discriminator Loss: 1.3154... Generator Loss: 0.7380
Epoch 2/10... Discriminator Loss: 1.2973... Generator Loss: 0.8427
Epoch 2/10... Discriminator Loss: 1.0916... Generator Loss: 1.0101
Epoch 2/10... Discriminator Loss: 1.2540... Generator Loss: 0.7468
Epoch 2/10... Discriminator Loss: 1.3736... Generator Loss: 0.5228
Epoch 2/10... Discriminator Loss: 1.2854... Generator Loss: 0.7515
Epoch 2/10... Discriminator Loss: 1.3197... Generator Loss: 0.6432
Epoch 2/10... Discriminator Loss: 1.2182... Generator Loss: 0.7248
Epoch 2/10... Discriminator Loss: 1.6023... Generator Loss: 0.3735
Epoch 2/10... Discriminator Loss: 1.3793... Generator Loss: 0.7012
Epoch 2/10... Discriminator Loss: 1.0843... Generator Loss: 0.9728
Epoch 2/10... Discriminator Loss: 1.5016... Generator Loss: 0.4173
Epoch 2/10... Discriminator Loss: 1.4589... Generator Loss: 0.4654
Epoch 2/10... Discriminator Loss: 1.2799... Generator Loss: 0.6473
Epoch 2/10... Discriminator Loss: 1.3934... Generator Loss: 0.7372
Epoch 2/10... Discriminator Loss: 1.3539... Generator Loss: 0.7036
Epoch 2/10... Discriminator Loss: 1.3909... Generator Loss: 0.4898
Epoch 2/10... Discriminator Loss: 1.3233... Generator Loss: 0.6107
Epoch 2/10... Discriminator Loss: 1.3300... Generator Loss: 0.5913
Epoch 2/10... Discriminator Loss: 1.2286... Generator Loss: 0.9042
Epoch 2/10... Discriminator Loss: 1.2655... Generator Loss: 0.7476
Epoch 2/10... Discriminator Loss: 1.3835... Generator Loss: 0.7184
Epoch 2/10... Discriminator Loss: 1.3256... Generator Loss: 0.6370
Epoch 3/10... Discriminator Loss: 1.2365... Generator Loss: 0.8102
Epoch 3/10... Discriminator Loss: 1.2631... Generator Loss: 0.9229
Epoch 3/10... Discriminator Loss: 1.2860... Generator Loss: 0.6512
Epoch 3/10... Discriminator Loss: 1.3406... Generator Loss: 0.7362
Epoch 3/10... Discriminator Loss: 1.3895... Generator Loss: 0.6632
Epoch 3/10... Discriminator Loss: 1.3262... Generator Loss: 0.7841
Epoch 3/10... Discriminator Loss: 1.2529... Generator Loss: 0.6025
Epoch 3/10... Discriminator Loss: 1.2192... Generator Loss: 0.7279
Epoch 3/10... Discriminator Loss: 1.1963... Generator Loss: 0.8286
Epoch 3/10... Discriminator Loss: 1.1819... Generator Loss: 0.7845
Epoch 3/10... Discriminator Loss: 1.4086... Generator Loss: 0.4710
Epoch 3/10... Discriminator Loss: 1.2510... Generator Loss: 0.7618
Epoch 3/10... Discriminator Loss: 1.3299... Generator Loss: 0.5426
Epoch 3/10... Discriminator Loss: 1.2587... Generator Loss: 0.8550
Epoch 3/10... Discriminator Loss: 1.3499... Generator Loss: 0.6446
Epoch 3/10... Discriminator Loss: 1.2917... Generator Loss: 0.6157
Epoch 3/10... Discriminator Loss: 1.2699... Generator Loss: 0.5627
Epoch 3/10... Discriminator Loss: 1.2686... Generator Loss: 0.9583
Epoch 3/10... Discriminator Loss: 1.2892... Generator Loss: 0.7400
Epoch 3/10... Discriminator Loss: 1.2955... Generator Loss: 0.6599
Epoch 3/10... Discriminator Loss: 1.2630... Generator Loss: 0.9687
Epoch 3/10... Discriminator Loss: 1.2784... Generator Loss: 0.8582
Epoch 3/10... Discriminator Loss: 1.4230... Generator Loss: 0.6189
Epoch 3/10... Discriminator Loss: 1.2981... Generator Loss: 0.8009
Epoch 3/10... Discriminator Loss: 1.3335... Generator Loss: 0.6214
Epoch 3/10... Discriminator Loss: 1.3037... Generator Loss: 0.6360
Epoch 3/10... Discriminator Loss: 1.4031... Generator Loss: 0.6054
Epoch 3/10... Discriminator Loss: 1.3155... Generator Loss: 0.5558
Epoch 3/10... Discriminator Loss: 1.2723... Generator Loss: 0.6378
Epoch 3/10... Discriminator Loss: 1.2261... Generator Loss: 0.7384
Epoch 3/10... Discriminator Loss: 1.1952... Generator Loss: 0.8932
Epoch 3/10... Discriminator Loss: 1.3337... Generator Loss: 0.6396
Epoch 3/10... Discriminator Loss: 1.2923... Generator Loss: 0.5783
Epoch 3/10... Discriminator Loss: 1.3845... Generator Loss: 0.6379
Epoch 3/10... Discriminator Loss: 1.1705... Generator Loss: 0.7925
Epoch 3/10... Discriminator Loss: 1.4938... Generator Loss: 0.4340
Epoch 3/10... Discriminator Loss: 1.3225... Generator Loss: 0.5788
Epoch 3/10... Discriminator Loss: 1.2916... Generator Loss: 0.9941
Epoch 3/10... Discriminator Loss: 1.6191... Generator Loss: 0.3617
Epoch 3/10... Discriminator Loss: 1.4834... Generator Loss: 0.4322
Epoch 3/10... Discriminator Loss: 1.1886... Generator Loss: 0.7322
Epoch 3/10... Discriminator Loss: 1.4187... Generator Loss: 0.4772
Epoch 3/10... Discriminator Loss: 1.2714... Generator Loss: 0.7600
Epoch 3/10... Discriminator Loss: 1.3506... Generator Loss: 0.5266
Epoch 3/10... Discriminator Loss: 1.2741... Generator Loss: 0.6845
Epoch 3/10... Discriminator Loss: 1.3686... Generator Loss: 0.6522
Epoch 3/10... Discriminator Loss: 1.5476... Generator Loss: 0.4144
Epoch 3/10... Discriminator Loss: 1.5018... Generator Loss: 0.4237
Epoch 3/10... Discriminator Loss: 1.3291... Generator Loss: 0.7145
Epoch 3/10... Discriminator Loss: 1.2165... Generator Loss: 0.7105
Epoch 3/10... Discriminator Loss: 1.2892... Generator Loss: 0.6897
Epoch 3/10... Discriminator Loss: 1.1618... Generator Loss: 0.8369
Epoch 3/10... Discriminator Loss: 1.1020... Generator Loss: 0.8949
Epoch 3/10... Discriminator Loss: 1.4166... Generator Loss: 1.1851
Epoch 3/10... Discriminator Loss: 1.3325... Generator Loss: 0.5529
Epoch 3/10... Discriminator Loss: 1.3906... Generator Loss: 0.6387
Epoch 3/10... Discriminator Loss: 1.3725... Generator Loss: 0.6135
Epoch 3/10... Discriminator Loss: 1.2794... Generator Loss: 0.6806
Epoch 3/10... Discriminator Loss: 1.2043... Generator Loss: 0.8246
Epoch 3/10... Discriminator Loss: 1.2643... Generator Loss: 0.6252
Epoch 3/10... Discriminator Loss: 1.2877... Generator Loss: 0.6761
Epoch 3/10... Discriminator Loss: 1.3445... Generator Loss: 0.5776
Epoch 3/10... Discriminator Loss: 1.3115... Generator Loss: 0.7118
Epoch 4/10... Discriminator Loss: 1.2850... Generator Loss: 0.5888
Epoch 4/10... Discriminator Loss: 1.4212... Generator Loss: 0.4526
Epoch 4/10... Discriminator Loss: 1.4017... Generator Loss: 0.5009
Epoch 4/10... Discriminator Loss: 1.3784... Generator Loss: 0.5470
Epoch 4/10... Discriminator Loss: 1.3963... Generator Loss: 0.4827
Epoch 4/10... Discriminator Loss: 1.1348... Generator Loss: 0.7993
Epoch 4/10... Discriminator Loss: 1.2219... Generator Loss: 0.7784
Epoch 4/10... Discriminator Loss: 1.0842... Generator Loss: 0.9096
Epoch 4/10... Discriminator Loss: 2.1113... Generator Loss: 0.2394
Epoch 4/10... Discriminator Loss: 1.2982... Generator Loss: 0.9657
Epoch 4/10... Discriminator Loss: 1.3483... Generator Loss: 0.5519
Epoch 4/10... Discriminator Loss: 1.2717... Generator Loss: 0.6332
Epoch 4/10... Discriminator Loss: 1.3630... Generator Loss: 0.8784
Epoch 4/10... Discriminator Loss: 1.6523... Generator Loss: 1.0567
Epoch 4/10... Discriminator Loss: 1.2462... Generator Loss: 0.7799
Epoch 4/10... Discriminator Loss: 1.3315... Generator Loss: 0.6385
Epoch 4/10... Discriminator Loss: 1.4634... Generator Loss: 0.4661
Epoch 4/10... Discriminator Loss: 1.2533... Generator Loss: 0.6840
Epoch 4/10... Discriminator Loss: 1.2692... Generator Loss: 0.6904
Epoch 4/10... Discriminator Loss: 1.2744... Generator Loss: 0.7779
Epoch 4/10... Discriminator Loss: 1.2545... Generator Loss: 0.5827
Epoch 4/10... Discriminator Loss: 1.4647... Generator Loss: 0.4722
Epoch 4/10... Discriminator Loss: 1.4627... Generator Loss: 0.4337
Epoch 4/10... Discriminator Loss: 1.2075... Generator Loss: 0.7013
Epoch 4/10... Discriminator Loss: 1.0307... Generator Loss: 1.1394
Epoch 4/10... Discriminator Loss: 1.3200... Generator Loss: 0.5977
Epoch 4/10... Discriminator Loss: 1.3227... Generator Loss: 0.5382
Epoch 4/10... Discriminator Loss: 1.3541... Generator Loss: 0.5472
Epoch 4/10... Discriminator Loss: 1.3740... Generator Loss: 0.4879
Epoch 4/10... Discriminator Loss: 1.3837... Generator Loss: 0.6043
Epoch 4/10... Discriminator Loss: 1.3985... Generator Loss: 0.8363
Epoch 4/10... Discriminator Loss: 1.1887... Generator Loss: 0.7454
Epoch 4/10... Discriminator Loss: 1.3759... Generator Loss: 0.5233
Epoch 4/10... Discriminator Loss: 1.2601... Generator Loss: 0.8075
Epoch 4/10... Discriminator Loss: 1.2418... Generator Loss: 0.7608
Epoch 4/10... Discriminator Loss: 1.2648... Generator Loss: 0.6270
Epoch 4/10... Discriminator Loss: 1.2830... Generator Loss: 0.6322
Epoch 4/10... Discriminator Loss: 1.3313... Generator Loss: 0.6368
Epoch 4/10... Discriminator Loss: 1.4839... Generator Loss: 0.4107
Epoch 4/10... Discriminator Loss: 1.3570... Generator Loss: 0.5614
Epoch 4/10... Discriminator Loss: 1.3401... Generator Loss: 0.8449
Epoch 4/10... Discriminator Loss: 1.3168... Generator Loss: 0.6124
Epoch 4/10... Discriminator Loss: 1.4567... Generator Loss: 0.4194
Epoch 4/10... Discriminator Loss: 1.2844... Generator Loss: 0.9369
Epoch 4/10... Discriminator Loss: 1.3006... Generator Loss: 0.9965
Epoch 4/10... Discriminator Loss: 1.1830... Generator Loss: 0.9136
Epoch 4/10... Discriminator Loss: 1.1937... Generator Loss: 0.7106
Epoch 4/10... Discriminator Loss: 1.3800... Generator Loss: 1.2523
Epoch 4/10... Discriminator Loss: 1.3618... Generator Loss: 0.4929
Epoch 4/10... Discriminator Loss: 1.3837... Generator Loss: 0.5438
Epoch 4/10... Discriminator Loss: 1.4132... Generator Loss: 0.4686
Epoch 4/10... Discriminator Loss: 1.2805... Generator Loss: 0.6267
Epoch 4/10... Discriminator Loss: 1.2974... Generator Loss: 0.7247
Epoch 4/10... Discriminator Loss: 1.1983... Generator Loss: 0.7536
Epoch 4/10... Discriminator Loss: 1.3514... Generator Loss: 0.6362
Epoch 4/10... Discriminator Loss: 1.3955... Generator Loss: 0.4913
Epoch 4/10... Discriminator Loss: 1.2138... Generator Loss: 0.8631
Epoch 4/10... Discriminator Loss: 1.4404... Generator Loss: 0.7626
Epoch 4/10... Discriminator Loss: 1.3557... Generator Loss: 0.7655
Epoch 4/10... Discriminator Loss: 1.2171... Generator Loss: 0.6371
Epoch 4/10... Discriminator Loss: 1.3864... Generator Loss: 0.5116
Epoch 4/10... Discriminator Loss: 1.2662... Generator Loss: 0.7556
Epoch 4/10... Discriminator Loss: 1.3627... Generator Loss: 0.6216
Epoch 4/10... Discriminator Loss: 1.3482... Generator Loss: 0.7833
Epoch 5/10... Discriminator Loss: 1.2702... Generator Loss: 0.5901
Epoch 5/10... Discriminator Loss: 1.3038... Generator Loss: 0.5884
Epoch 5/10... Discriminator Loss: 1.1580... Generator Loss: 0.9423
Epoch 5/10... Discriminator Loss: 1.3950... Generator Loss: 0.6296
Epoch 5/10... Discriminator Loss: 1.4454... Generator Loss: 0.4359
Epoch 5/10... Discriminator Loss: 1.1842... Generator Loss: 0.6722
Epoch 5/10... Discriminator Loss: 1.3576... Generator Loss: 0.5441
Epoch 5/10... Discriminator Loss: 1.3452... Generator Loss: 0.7763
Epoch 5/10... Discriminator Loss: 1.5196... Generator Loss: 0.4146
Epoch 5/10... Discriminator Loss: 1.3229... Generator Loss: 0.6701
Epoch 5/10... Discriminator Loss: 1.2627... Generator Loss: 0.7176
Epoch 5/10... Discriminator Loss: 1.3214... Generator Loss: 0.5642
Epoch 5/10... Discriminator Loss: 1.2717... Generator Loss: 0.6445
Epoch 5/10... Discriminator Loss: 1.2484... Generator Loss: 0.6106
Epoch 5/10... Discriminator Loss: 1.2736... Generator Loss: 0.9640
Epoch 5/10... Discriminator Loss: 1.3747... Generator Loss: 0.6129
Epoch 5/10... Discriminator Loss: 1.5843... Generator Loss: 0.3784
Epoch 5/10... Discriminator Loss: 1.4198... Generator Loss: 0.4537
Epoch 5/10... Discriminator Loss: 1.3499... Generator Loss: 0.5463
Epoch 5/10... Discriminator Loss: 1.4511... Generator Loss: 0.4582
Epoch 5/10... Discriminator Loss: 1.2349... Generator Loss: 0.8062
Epoch 5/10... Discriminator Loss: 1.3608... Generator Loss: 0.6762
Epoch 5/10... Discriminator Loss: 1.2387... Generator Loss: 0.6373
Epoch 5/10... Discriminator Loss: 1.2401... Generator Loss: 0.8938
Epoch 5/10... Discriminator Loss: 1.1941... Generator Loss: 0.7340
Epoch 5/10... Discriminator Loss: 1.2382... Generator Loss: 0.8599
Epoch 5/10... Discriminator Loss: 1.2569... Generator Loss: 0.6739
Epoch 5/10... Discriminator Loss: 1.3179... Generator Loss: 0.7142
Epoch 5/10... Discriminator Loss: 1.4152... Generator Loss: 0.4679
Epoch 5/10... Discriminator Loss: 1.3760... Generator Loss: 0.6295
Epoch 5/10... Discriminator Loss: 1.2940... Generator Loss: 0.7108
Epoch 5/10... Discriminator Loss: 1.2919... Generator Loss: 0.6639
Epoch 5/10... Discriminator Loss: 1.3499... Generator Loss: 0.5069
Epoch 5/10... Discriminator Loss: 1.2266... Generator Loss: 0.6459
Epoch 5/10... Discriminator Loss: 1.3624... Generator Loss: 0.8224
Epoch 5/10... Discriminator Loss: 1.4341... Generator Loss: 0.4435
Epoch 5/10... Discriminator Loss: 1.3477... Generator Loss: 0.5531
Epoch 5/10... Discriminator Loss: 1.3389... Generator Loss: 0.5369
Epoch 5/10... Discriminator Loss: 1.3051... Generator Loss: 0.7608
Epoch 5/10... Discriminator Loss: 1.2647... Generator Loss: 0.7525
Epoch 5/10... Discriminator Loss: 1.5979... Generator Loss: 0.4606
Epoch 5/10... Discriminator Loss: 1.5341... Generator Loss: 0.4006
Epoch 5/10... Discriminator Loss: 1.2113... Generator Loss: 0.7000
Epoch 5/10... Discriminator Loss: 1.2257... Generator Loss: 0.6890
Epoch 5/10... Discriminator Loss: 1.2719... Generator Loss: 0.7637
Epoch 5/10... Discriminator Loss: 1.2738... Generator Loss: 0.7385
Epoch 5/10... Discriminator Loss: 1.2422... Generator Loss: 0.6121
Epoch 5/10... Discriminator Loss: 1.2672... Generator Loss: 0.7288
Epoch 5/10... Discriminator Loss: 1.3306... Generator Loss: 0.7762
Epoch 5/10... Discriminator Loss: 1.3160... Generator Loss: 0.5644
Epoch 5/10... Discriminator Loss: 1.2612... Generator Loss: 0.5846
Epoch 5/10... Discriminator Loss: 1.4332... Generator Loss: 0.4599
Epoch 5/10... Discriminator Loss: 1.2836... Generator Loss: 0.6512
Epoch 5/10... Discriminator Loss: 1.2389... Generator Loss: 0.7061
Epoch 5/10... Discriminator Loss: 1.2573... Generator Loss: 0.6673
Epoch 5/10... Discriminator Loss: 1.2692... Generator Loss: 0.7044
Epoch 5/10... Discriminator Loss: 1.8014... Generator Loss: 0.2963
Epoch 5/10... Discriminator Loss: 1.5575... Generator Loss: 0.3778
Epoch 5/10... Discriminator Loss: 1.1977... Generator Loss: 0.7068
Epoch 5/10... Discriminator Loss: 1.3858... Generator Loss: 0.5335
Epoch 5/10... Discriminator Loss: 1.2260... Generator Loss: 0.8675
Epoch 5/10... Discriminator Loss: 1.2683... Generator Loss: 0.9277
Epoch 5/10... Discriminator Loss: 1.2999... Generator Loss: 0.6238
Epoch 6/10... Discriminator Loss: 1.3142... Generator Loss: 0.6859
Epoch 6/10... Discriminator Loss: 1.3740... Generator Loss: 0.5318
Epoch 6/10... Discriminator Loss: 1.2821... Generator Loss: 0.7179
Epoch 6/10... Discriminator Loss: 1.4475... Generator Loss: 0.6150
Epoch 6/10... Discriminator Loss: 1.4281... Generator Loss: 0.6807
Epoch 6/10... Discriminator Loss: 1.2248... Generator Loss: 0.7922
Epoch 6/10... Discriminator Loss: 1.2254... Generator Loss: 0.6376
Epoch 6/10... Discriminator Loss: 1.2626... Generator Loss: 0.7358
Epoch 6/10... Discriminator Loss: 1.4750... Generator Loss: 0.4265
Epoch 6/10... Discriminator Loss: 1.2261... Generator Loss: 0.7710
Epoch 6/10... Discriminator Loss: 1.4079... Generator Loss: 0.4593
Epoch 6/10... Discriminator Loss: 1.2907... Generator Loss: 0.8069
Epoch 6/10... Discriminator Loss: 1.3905... Generator Loss: 0.7027
Epoch 6/10... Discriminator Loss: 1.3459... Generator Loss: 0.5835
Epoch 6/10... Discriminator Loss: 1.2698... Generator Loss: 0.6455
Epoch 6/10... Discriminator Loss: 1.1240... Generator Loss: 0.8941
Epoch 6/10... Discriminator Loss: 1.3647... Generator Loss: 0.6542
Epoch 6/10... Discriminator Loss: 1.2971... Generator Loss: 0.6831
Epoch 6/10... Discriminator Loss: 1.3369... Generator Loss: 0.6856
Epoch 6/10... Discriminator Loss: 1.2960... Generator Loss: 0.8458
Epoch 6/10... Discriminator Loss: 1.3860... Generator Loss: 0.4778
Epoch 6/10... Discriminator Loss: 1.6584... Generator Loss: 0.3416
Epoch 6/10... Discriminator Loss: 1.2510... Generator Loss: 0.6954
Epoch 6/10... Discriminator Loss: 1.3503... Generator Loss: 0.5327
Epoch 6/10... Discriminator Loss: 1.1523... Generator Loss: 0.6889
Epoch 6/10... Discriminator Loss: 1.3459... Generator Loss: 0.5080
Epoch 6/10... Discriminator Loss: 1.3445... Generator Loss: 0.6027
Epoch 6/10... Discriminator Loss: 0.9995... Generator Loss: 0.9360
Epoch 6/10... Discriminator Loss: 1.3493... Generator Loss: 0.6188
Epoch 6/10... Discriminator Loss: 1.2329... Generator Loss: 0.5794
Epoch 6/10... Discriminator Loss: 1.5417... Generator Loss: 0.3951
Epoch 6/10... Discriminator Loss: 1.1264... Generator Loss: 0.7748
Epoch 6/10... Discriminator Loss: 1.3732... Generator Loss: 0.5013
Epoch 6/10... Discriminator Loss: 1.2460... Generator Loss: 0.6814
Epoch 6/10... Discriminator Loss: 1.3728... Generator Loss: 0.5370
Epoch 6/10... Discriminator Loss: 1.3353... Generator Loss: 0.6404
Epoch 6/10... Discriminator Loss: 1.3531... Generator Loss: 0.6041
Epoch 6/10... Discriminator Loss: 1.2668... Generator Loss: 0.6820
Epoch 6/10... Discriminator Loss: 1.5077... Generator Loss: 0.4075
Epoch 6/10... Discriminator Loss: 1.3890... Generator Loss: 0.4902
Epoch 6/10... Discriminator Loss: 1.4217... Generator Loss: 0.6330
Epoch 6/10... Discriminator Loss: 1.2468... Generator Loss: 0.6786
Epoch 6/10... Discriminator Loss: 1.2204... Generator Loss: 0.8092
Epoch 6/10... Discriminator Loss: 1.3037... Generator Loss: 0.6141
Epoch 6/10... Discriminator Loss: 1.2323... Generator Loss: 0.6911
Epoch 6/10... Discriminator Loss: 1.4179... Generator Loss: 0.6041
Epoch 6/10... Discriminator Loss: 1.1530... Generator Loss: 0.7331
Epoch 6/10... Discriminator Loss: 1.3128... Generator Loss: 0.7778
Epoch 6/10... Discriminator Loss: 1.3870... Generator Loss: 0.5222
Epoch 6/10... Discriminator Loss: 1.5000... Generator Loss: 0.4099
Epoch 6/10... Discriminator Loss: 1.4035... Generator Loss: 0.4664
Epoch 6/10... Discriminator Loss: 1.3196... Generator Loss: 0.5418
Epoch 6/10... Discriminator Loss: 1.3970... Generator Loss: 0.4673
Epoch 6/10... Discriminator Loss: 1.2270... Generator Loss: 0.6669
Epoch 6/10... Discriminator Loss: 1.1531... Generator Loss: 0.8052
Epoch 6/10... Discriminator Loss: 1.2096... Generator Loss: 0.6376
Epoch 6/10... Discriminator Loss: 1.2418... Generator Loss: 0.7078
Epoch 6/10... Discriminator Loss: 1.3838... Generator Loss: 0.7193
Epoch 6/10... Discriminator Loss: 1.3099... Generator Loss: 0.5576
Epoch 6/10... Discriminator Loss: 1.1750... Generator Loss: 0.7010
Epoch 6/10... Discriminator Loss: 1.4349... Generator Loss: 0.4686
Epoch 6/10... Discriminator Loss: 1.1265... Generator Loss: 0.8666
Epoch 6/10... Discriminator Loss: 1.3759... Generator Loss: 0.5024
Epoch 7/10... Discriminator Loss: 1.3633... Generator Loss: 0.5504
Epoch 7/10... Discriminator Loss: 1.2799... Generator Loss: 1.0125
Epoch 7/10... Discriminator Loss: 1.3261... Generator Loss: 0.6870
Epoch 7/10... Discriminator Loss: 1.3281... Generator Loss: 0.6642
Epoch 7/10... Discriminator Loss: 1.8994... Generator Loss: 0.2816
Epoch 7/10... Discriminator Loss: 1.3440... Generator Loss: 0.6685
Epoch 7/10... Discriminator Loss: 1.2440... Generator Loss: 0.7106
Epoch 7/10... Discriminator Loss: 1.4951... Generator Loss: 0.4145
Epoch 7/10... Discriminator Loss: 1.3349... Generator Loss: 0.5206
Epoch 7/10... Discriminator Loss: 1.5012... Generator Loss: 0.4054
Epoch 7/10... Discriminator Loss: 1.3656... Generator Loss: 0.5921
Epoch 7/10... Discriminator Loss: 1.3937... Generator Loss: 0.5219
Epoch 7/10... Discriminator Loss: 1.3830... Generator Loss: 0.4820
Epoch 7/10... Discriminator Loss: 1.3364... Generator Loss: 0.6757
Epoch 7/10... Discriminator Loss: 1.4289... Generator Loss: 0.4369
Epoch 7/10... Discriminator Loss: 1.4178... Generator Loss: 0.5946
Epoch 7/10... Discriminator Loss: 1.3829... Generator Loss: 0.4798
Epoch 7/10... Discriminator Loss: 1.7112... Generator Loss: 0.7505
Epoch 7/10... Discriminator Loss: 1.4980... Generator Loss: 0.4218
Epoch 7/10... Discriminator Loss: 1.3525... Generator Loss: 0.5030
Epoch 7/10... Discriminator Loss: 1.1916... Generator Loss: 0.8318
Epoch 7/10... Discriminator Loss: 1.2550... Generator Loss: 0.9622
Epoch 7/10... Discriminator Loss: 1.3002... Generator Loss: 0.6126
Epoch 7/10... Discriminator Loss: 1.2522... Generator Loss: 0.6227
Epoch 7/10... Discriminator Loss: 1.4865... Generator Loss: 0.4116
Epoch 7/10... Discriminator Loss: 1.2860... Generator Loss: 0.8820
Epoch 7/10... Discriminator Loss: 1.2051... Generator Loss: 0.7750
Epoch 7/10... Discriminator Loss: 1.2581... Generator Loss: 0.7947
Epoch 7/10... Discriminator Loss: 1.3056... Generator Loss: 0.7760
Epoch 7/10... Discriminator Loss: 1.2531... Generator Loss: 0.7174
Epoch 7/10... Discriminator Loss: 1.3605... Generator Loss: 0.5343
Epoch 7/10... Discriminator Loss: 1.3711... Generator Loss: 0.5078
Epoch 7/10... Discriminator Loss: 1.3876... Generator Loss: 0.5410
Epoch 7/10... Discriminator Loss: 1.3396... Generator Loss: 0.5285
Epoch 7/10... Discriminator Loss: 1.1930... Generator Loss: 0.7476
Epoch 7/10... Discriminator Loss: 1.3746... Generator Loss: 0.6706
Epoch 7/10... Discriminator Loss: 1.2413... Generator Loss: 0.8610
Epoch 7/10... Discriminator Loss: 1.2084... Generator Loss: 0.9553
Epoch 7/10... Discriminator Loss: 1.3802... Generator Loss: 0.4805
Epoch 7/10... Discriminator Loss: 1.2642... Generator Loss: 0.7023
Epoch 7/10... Discriminator Loss: 1.2281... Generator Loss: 1.0007
Epoch 7/10... Discriminator Loss: 1.3810... Generator Loss: 0.8387
Epoch 7/10... Discriminator Loss: 1.3674... Generator Loss: 0.5317
Epoch 7/10... Discriminator Loss: 1.3192... Generator Loss: 0.5301
Epoch 7/10... Discriminator Loss: 1.3852... Generator Loss: 0.4710
Epoch 7/10... Discriminator Loss: 1.3061... Generator Loss: 0.8750
Epoch 7/10... Discriminator Loss: 1.3390... Generator Loss: 0.6235
Epoch 7/10... Discriminator Loss: 1.3569... Generator Loss: 0.5068
Epoch 7/10... Discriminator Loss: 1.2851... Generator Loss: 0.9950
Epoch 7/10... Discriminator Loss: 1.2955... Generator Loss: 0.7841
Epoch 7/10... Discriminator Loss: 1.2051... Generator Loss: 0.6197
Epoch 7/10... Discriminator Loss: 1.3803... Generator Loss: 0.4792
Epoch 7/10... Discriminator Loss: 1.3901... Generator Loss: 0.5094
Epoch 7/10... Discriminator Loss: 1.3947... Generator Loss: 0.4655
Epoch 7/10... Discriminator Loss: 1.3050... Generator Loss: 0.8227
Epoch 7/10... Discriminator Loss: 1.2857... Generator Loss: 0.7511
Epoch 7/10... Discriminator Loss: 1.3569... Generator Loss: 0.5103
Epoch 7/10... Discriminator Loss: 1.1763... Generator Loss: 0.7362
Epoch 7/10... Discriminator Loss: 1.3018... Generator Loss: 0.5856
Epoch 7/10... Discriminator Loss: 1.2450... Generator Loss: 0.8740
Epoch 7/10... Discriminator Loss: 1.1310... Generator Loss: 0.7898
Epoch 7/10... Discriminator Loss: 1.3563... Generator Loss: 0.6687
Epoch 7/10... Discriminator Loss: 1.4062... Generator Loss: 0.5641
Epoch 8/10... Discriminator Loss: 1.3730... Generator Loss: 0.4923
Epoch 8/10... Discriminator Loss: 1.3257... Generator Loss: 0.6198
Epoch 8/10... Discriminator Loss: 1.2519... Generator Loss: 0.6717
Epoch 8/10... Discriminator Loss: 1.3204... Generator Loss: 0.5414
Epoch 8/10... Discriminator Loss: 1.0947... Generator Loss: 0.8441
Epoch 8/10... Discriminator Loss: 1.3395... Generator Loss: 0.5576
Epoch 8/10... Discriminator Loss: 1.2002... Generator Loss: 0.6520
Epoch 8/10... Discriminator Loss: 1.3500... Generator Loss: 0.6920
Epoch 8/10... Discriminator Loss: 1.5531... Generator Loss: 0.3911
Epoch 8/10... Discriminator Loss: 1.4125... Generator Loss: 0.5256
Epoch 8/10... Discriminator Loss: 1.4162... Generator Loss: 0.4674
Epoch 8/10... Discriminator Loss: 1.4136... Generator Loss: 0.4534
Epoch 8/10... Discriminator Loss: 1.3662... Generator Loss: 0.5356
Epoch 8/10... Discriminator Loss: 1.2388... Generator Loss: 0.6711
Epoch 8/10... Discriminator Loss: 1.1801... Generator Loss: 0.9184
Epoch 8/10... Discriminator Loss: 1.2821... Generator Loss: 0.5963
Epoch 8/10... Discriminator Loss: 1.3302... Generator Loss: 0.5613
Epoch 8/10... Discriminator Loss: 1.3438... Generator Loss: 0.4998
Epoch 8/10... Discriminator Loss: 1.3236... Generator Loss: 0.5561
Epoch 8/10... Discriminator Loss: 1.4958... Generator Loss: 0.4170
Epoch 8/10... Discriminator Loss: 1.3212... Generator Loss: 0.6296
Epoch 8/10... Discriminator Loss: 1.3579... Generator Loss: 0.7875
Epoch 8/10... Discriminator Loss: 1.3914... Generator Loss: 0.5116
Epoch 8/10... Discriminator Loss: 1.2219... Generator Loss: 0.6628
Epoch 8/10... Discriminator Loss: 1.2750... Generator Loss: 0.6479
Epoch 8/10... Discriminator Loss: 1.3191... Generator Loss: 0.6952
Epoch 8/10... Discriminator Loss: 1.2947... Generator Loss: 0.9502
Epoch 8/10... Discriminator Loss: 1.2354... Generator Loss: 0.5862
Epoch 8/10... Discriminator Loss: 1.5153... Generator Loss: 0.7384
Epoch 8/10... Discriminator Loss: 1.3680... Generator Loss: 0.5529
Epoch 8/10... Discriminator Loss: 1.3241... Generator Loss: 0.8317
Epoch 8/10... Discriminator Loss: 1.3414... Generator Loss: 0.5575
Epoch 8/10... Discriminator Loss: 1.4840... Generator Loss: 0.4134
Epoch 8/10... Discriminator Loss: 1.3765... Generator Loss: 0.4896
Epoch 8/10... Discriminator Loss: 1.2940... Generator Loss: 0.6599
Epoch 8/10... Discriminator Loss: 1.2847... Generator Loss: 0.5754
Epoch 8/10... Discriminator Loss: 1.1456... Generator Loss: 0.7084
Epoch 8/10... Discriminator Loss: 1.2718... Generator Loss: 0.8636
Epoch 8/10... Discriminator Loss: 1.2142... Generator Loss: 0.9349
Epoch 8/10... Discriminator Loss: 1.2114... Generator Loss: 0.6715
Epoch 8/10... Discriminator Loss: 1.6071... Generator Loss: 0.3491
Epoch 8/10... Discriminator Loss: 1.4010... Generator Loss: 0.4602
Epoch 8/10... Discriminator Loss: 1.5528... Generator Loss: 0.3767
Epoch 8/10... Discriminator Loss: 1.3721... Generator Loss: 0.5591
Epoch 8/10... Discriminator Loss: 1.3016... Generator Loss: 0.6535
Epoch 8/10... Discriminator Loss: 1.2931... Generator Loss: 0.5777
Epoch 8/10... Discriminator Loss: 1.5432... Generator Loss: 0.3838
Epoch 8/10... Discriminator Loss: 1.3434... Generator Loss: 0.7264
Epoch 8/10... Discriminator Loss: 1.2309... Generator Loss: 0.6142
Epoch 8/10... Discriminator Loss: 1.3983... Generator Loss: 0.4643
Epoch 8/10... Discriminator Loss: 1.3401... Generator Loss: 0.5727
Epoch 8/10... Discriminator Loss: 1.3207... Generator Loss: 0.8406
Epoch 8/10... Discriminator Loss: 1.4760... Generator Loss: 0.4192
Epoch 8/10... Discriminator Loss: 1.3397... Generator Loss: 0.5816
Epoch 8/10... Discriminator Loss: 1.3224... Generator Loss: 0.6738
Epoch 8/10... Discriminator Loss: 1.5342... Generator Loss: 0.3958
Epoch 8/10... Discriminator Loss: 1.2761... Generator Loss: 0.6457
Epoch 8/10... Discriminator Loss: 1.3424... Generator Loss: 0.8279
Epoch 8/10... Discriminator Loss: 1.3618... Generator Loss: 0.8118
Epoch 8/10... Discriminator Loss: 1.3419... Generator Loss: 0.6092
Epoch 8/10... Discriminator Loss: 1.1941... Generator Loss: 0.8841
Epoch 8/10... Discriminator Loss: 1.2773... Generator Loss: 0.7115
Epoch 8/10... Discriminator Loss: 1.4310... Generator Loss: 0.4599
Epoch 8/10... Discriminator Loss: 1.0448... Generator Loss: 0.9075
Epoch 9/10... Discriminator Loss: 1.2844... Generator Loss: 0.6400
Epoch 9/10... Discriminator Loss: 1.1997... Generator Loss: 0.7711
Epoch 9/10... Discriminator Loss: 1.2748... Generator Loss: 0.6046
Epoch 9/10... Discriminator Loss: 1.0655... Generator Loss: 1.0052
Epoch 9/10... Discriminator Loss: 1.1810... Generator Loss: 0.7979
Epoch 9/10... Discriminator Loss: 1.1565... Generator Loss: 0.7251
Epoch 9/10... Discriminator Loss: 1.4171... Generator Loss: 0.4517
Epoch 9/10... Discriminator Loss: 1.2928... Generator Loss: 0.6505
Epoch 9/10... Discriminator Loss: 1.2508... Generator Loss: 0.6408
Epoch 9/10... Discriminator Loss: 1.5991... Generator Loss: 0.3734
Epoch 9/10... Discriminator Loss: 1.3830... Generator Loss: 0.4937
Epoch 9/10... Discriminator Loss: 1.5594... Generator Loss: 0.3822
Epoch 9/10... Discriminator Loss: 1.2050... Generator Loss: 0.6283
Epoch 9/10... Discriminator Loss: 1.2720... Generator Loss: 0.6391
Epoch 9/10... Discriminator Loss: 1.4661... Generator Loss: 0.4242
Epoch 9/10... Discriminator Loss: 1.2834... Generator Loss: 0.6291
Epoch 9/10... Discriminator Loss: 1.1469... Generator Loss: 0.6896
Epoch 9/10... Discriminator Loss: 1.3154... Generator Loss: 0.5327
Epoch 9/10... Discriminator Loss: 1.4994... Generator Loss: 0.4066
Epoch 9/10... Discriminator Loss: 1.4122... Generator Loss: 0.9728
Epoch 9/10... Discriminator Loss: 1.1746... Generator Loss: 0.7635
Epoch 9/10... Discriminator Loss: 1.5060... Generator Loss: 0.5502
Epoch 9/10... Discriminator Loss: 1.1307... Generator Loss: 0.7740
Epoch 9/10... Discriminator Loss: 1.4546... Generator Loss: 0.4356
Epoch 9/10... Discriminator Loss: 1.4945... Generator Loss: 0.5083
Epoch 9/10... Discriminator Loss: 1.3315... Generator Loss: 0.5587
Epoch 9/10... Discriminator Loss: 1.0956... Generator Loss: 0.8201
Epoch 9/10... Discriminator Loss: 1.2215... Generator Loss: 0.5781
Epoch 9/10... Discriminator Loss: 1.2030... Generator Loss: 0.8351
Epoch 9/10... Discriminator Loss: 1.7629... Generator Loss: 0.3102
Epoch 9/10... Discriminator Loss: 1.2083... Generator Loss: 0.8200
Epoch 9/10... Discriminator Loss: 1.2043... Generator Loss: 0.6852
Epoch 9/10... Discriminator Loss: 1.4166... Generator Loss: 0.4608
Epoch 9/10... Discriminator Loss: 1.2127... Generator Loss: 0.6360
Epoch 9/10... Discriminator Loss: 1.2683... Generator Loss: 0.7104
Epoch 9/10... Discriminator Loss: 1.3028... Generator Loss: 0.5546
Epoch 9/10... Discriminator Loss: 1.2665... Generator Loss: 0.6729
Epoch 9/10... Discriminator Loss: 1.3387... Generator Loss: 0.6966
Epoch 9/10... Discriminator Loss: 1.3160... Generator Loss: 0.5284
Epoch 9/10... Discriminator Loss: 1.1534... Generator Loss: 0.6938
Epoch 9/10... Discriminator Loss: 1.1704... Generator Loss: 0.7316
Epoch 9/10... Discriminator Loss: 1.5991... Generator Loss: 0.3618
Epoch 9/10... Discriminator Loss: 1.1095... Generator Loss: 0.7559
Epoch 9/10... Discriminator Loss: 1.2226... Generator Loss: 0.7570
Epoch 9/10... Discriminator Loss: 1.3381... Generator Loss: 0.5902
Epoch 9/10... Discriminator Loss: 1.3293... Generator Loss: 0.5894
Epoch 9/10... Discriminator Loss: 1.4076... Generator Loss: 0.5358
Epoch 9/10... Discriminator Loss: 1.3806... Generator Loss: 0.5705
Epoch 9/10... Discriminator Loss: 1.1893... Generator Loss: 1.0118
Epoch 9/10... Discriminator Loss: 1.2843... Generator Loss: 0.5877
Epoch 9/10... Discriminator Loss: 1.4680... Generator Loss: 0.4523
Epoch 9/10... Discriminator Loss: 1.3594... Generator Loss: 0.5841
Epoch 9/10... Discriminator Loss: 1.3547... Generator Loss: 0.6720
Epoch 9/10... Discriminator Loss: 1.6044... Generator Loss: 0.3578
Epoch 9/10... Discriminator Loss: 1.3932... Generator Loss: 0.5130
Epoch 9/10... Discriminator Loss: 1.2857... Generator Loss: 0.8814
Epoch 9/10... Discriminator Loss: 1.3395... Generator Loss: 0.5219
Epoch 9/10... Discriminator Loss: 1.3939... Generator Loss: 0.4884
Epoch 9/10... Discriminator Loss: 1.3733... Generator Loss: 0.5189
Epoch 9/10... Discriminator Loss: 1.2761... Generator Loss: 0.5537
Epoch 9/10... Discriminator Loss: 1.1695... Generator Loss: 0.7726
Epoch 9/10... Discriminator Loss: 1.3573... Generator Loss: 0.6162
Epoch 9/10... Discriminator Loss: 1.2978... Generator Loss: 0.8424
Epoch 10/10... Discriminator Loss: 1.2403... Generator Loss: 0.6547
Epoch 10/10... Discriminator Loss: 1.0476... Generator Loss: 0.8997
Epoch 10/10... Discriminator Loss: 1.6859... Generator Loss: 0.3309
Epoch 10/10... Discriminator Loss: 1.1957... Generator Loss: 0.7014
Epoch 10/10... Discriminator Loss: 1.2725... Generator Loss: 0.9579
Epoch 10/10... Discriminator Loss: 1.3661... Generator Loss: 0.5524
Epoch 10/10... Discriminator Loss: 1.3309... Generator Loss: 0.5158
Epoch 10/10... Discriminator Loss: 1.4976... Generator Loss: 0.4135
Epoch 10/10... Discriminator Loss: 1.2983... Generator Loss: 0.8340
Epoch 10/10... Discriminator Loss: 1.3247... Generator Loss: 0.5150
Epoch 10/10... Discriminator Loss: 1.3793... Generator Loss: 0.8863
Epoch 10/10... Discriminator Loss: 1.2807... Generator Loss: 0.5515
Epoch 10/10... Discriminator Loss: 1.1405... Generator Loss: 0.8036
Epoch 10/10... Discriminator Loss: 1.1337... Generator Loss: 0.8492
Epoch 10/10... Discriminator Loss: 1.4467... Generator Loss: 0.4573
Epoch 10/10... Discriminator Loss: 1.3712... Generator Loss: 0.5020
Epoch 10/10... Discriminator Loss: 1.2371... Generator Loss: 0.7379
Epoch 10/10... Discriminator Loss: 1.2605... Generator Loss: 0.6756
Epoch 10/10... Discriminator Loss: 1.4169... Generator Loss: 0.5160
Epoch 10/10... Discriminator Loss: 1.2865... Generator Loss: 0.5702
Epoch 10/10... Discriminator Loss: 1.1691... Generator Loss: 0.7374
Epoch 10/10... Discriminator Loss: 1.4158... Generator Loss: 0.5157
Epoch 10/10... Discriminator Loss: 1.2951... Generator Loss: 0.7696
Epoch 10/10... Discriminator Loss: 1.1936... Generator Loss: 0.7515
Epoch 10/10... Discriminator Loss: 1.3073... Generator Loss: 0.5459
Epoch 10/10... Discriminator Loss: 1.2616... Generator Loss: 0.6519
Epoch 10/10... Discriminator Loss: 1.2485... Generator Loss: 0.6852
Epoch 10/10... Discriminator Loss: 1.1531... Generator Loss: 0.7745
Epoch 10/10... Discriminator Loss: 1.3280... Generator Loss: 0.5720
Epoch 10/10... Discriminator Loss: 1.3112... Generator Loss: 0.5467
Epoch 10/10... Discriminator Loss: 1.4194... Generator Loss: 0.4509
Epoch 10/10... Discriminator Loss: 1.1552... Generator Loss: 0.8906
Epoch 10/10... Discriminator Loss: 1.2149... Generator Loss: 0.6769
Epoch 10/10... Discriminator Loss: 1.1959... Generator Loss: 0.7086
Epoch 10/10... Discriminator Loss: 1.5136... Generator Loss: 0.4028
Epoch 10/10... Discriminator Loss: 1.2536... Generator Loss: 0.6459
Epoch 10/10... Discriminator Loss: 1.1937... Generator Loss: 0.7507
Epoch 10/10... Discriminator Loss: 1.2990... Generator Loss: 0.6698
Epoch 10/10... Discriminator Loss: 1.6610... Generator Loss: 0.3332
Epoch 10/10... Discriminator Loss: 1.2833... Generator Loss: 0.6759
Epoch 10/10... Discriminator Loss: 1.2385... Generator Loss: 0.6430
Epoch 10/10... Discriminator Loss: 1.2805... Generator Loss: 0.5606
Epoch 10/10... Discriminator Loss: 1.3455... Generator Loss: 0.5052
Epoch 10/10... Discriminator Loss: 1.4487... Generator Loss: 0.5122
Epoch 10/10... Discriminator Loss: 1.3224... Generator Loss: 0.5564
Epoch 10/10... Discriminator Loss: 1.2649... Generator Loss: 0.6022
Epoch 10/10... Discriminator Loss: 1.3580... Generator Loss: 0.5503
Epoch 10/10... Discriminator Loss: 1.2278... Generator Loss: 0.7662
Epoch 10/10... Discriminator Loss: 1.2455... Generator Loss: 0.6765
Epoch 10/10... Discriminator Loss: 1.2047... Generator Loss: 0.6302
Epoch 10/10... Discriminator Loss: 1.3755... Generator Loss: 0.4668
Epoch 10/10... Discriminator Loss: 1.3714... Generator Loss: 0.5340
Epoch 10/10... Discriminator Loss: 1.0982... Generator Loss: 0.9331
Epoch 10/10... Discriminator Loss: 1.2618... Generator Loss: 0.5770
Epoch 10/10... Discriminator Loss: 1.6204... Generator Loss: 1.2045
Epoch 10/10... Discriminator Loss: 1.2375... Generator Loss: 0.6060
Epoch 10/10... Discriminator Loss: 1.2096... Generator Loss: 1.1169
Epoch 10/10... Discriminator Loss: 1.1932... Generator Loss: 0.7295
Epoch 10/10... Discriminator Loss: 1.4731... Generator Loss: 0.4268
Epoch 10/10... Discriminator Loss: 1.0935... Generator Loss: 0.8008
Epoch 10/10... Discriminator Loss: 1.5471... Generator Loss: 0.3855
Epoch 10/10... Discriminator Loss: 1.2638... Generator Loss: 0.6036
Epoch 10/10... Discriminator Loss: 1.3971... Generator Loss: 0.4692

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.